Pubblicato su stage4eu il: 26/03/2019 Amazon, Applied Scientist Intern

Amazon Europe
33, Rives de Clausen, Luxembourg, Lussemburgo
Statistica/Data analysis
3-6 mesi 
Posti disponibili Non specificato
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Attività:

Our team is focused on creating the best-in-class experience for customers to discover and shop for home furnishings online, across devices. We develop innovative machine learning-based solutions in application domains like computer vision and text processing. To this end, we collaborate closely with other science and product teams across Amazon.

As part of the internship you will be a full member of our team working with other scientists, software engineers, product managers and UX specialists on completing a self-contained internship project. Specific challenges will be dealing with very large heterogeneous datasets, designing novel algorithms that are robust and reliable, and efficient implementations that can run in production.

Requisiti principali:

We are looking for Master's and PhD students to join the EU Home Innovation Program.

Basic qualifications:  

  • On-track for graduate or postgraduate degree in Machine Learning, Data Science, Statistics or related field.
  • Hands-on experience in one or more of the following areas: deep learning, probabilistic modelling, statistical machine learning, data processing & data analytics, computer vision.
  • Enthusiasm for applying machine learning to real-world problems.
  • Computer science grounding in a range of algorithms, data structures, and programming languages.
  • Ability to present your beliefs clearly and compellingly in both verbal and written form.

Preferred qualifications: 

  • Ability to convey rigorous mathematical concepts and considerations to non-experts.
  • Ability to distill problem definitions, models, and constraints from informal business requirements, and to deal with ambiguity and competing objectives.
  • Strong software development skills; experience in Python is a plus.
  • Publications in top-tier science conferences and journals; experience with a deep learning framework.